In real-world scenarios, users usually have multiple intents in the same
utterance. Unfortunately, most spoken language understanding (SLU) models
either mainly focused on the single intent scenario, or simply incorporated an
overall intent context vector for all tokens, ignoring the fine-grained
multiple intents information integration for token-level slot prediction. In
this paper, we propose an Adaptive Graph-Interactive Framework (AGIF) for joint
multiple intent detection and slot filling, where we introduce an intent-slot
graph interaction layer to model the strong correlation between the slot and
intents. Such an interaction layer is applied to each token adaptively, which
has the advantage to automatically extract the relevant intents information,
making a fine-grained intent information integration for the token-level slot
prediction. Experimental results on three multi-intent datasets show that our
framework obtains substantial improvement and achieves the state-of-the-art
performance. In addition, our framework achieves new state-of-the-art
performance on two single-intent datasets.

本文提出了一个自适应图交互框架（AGIF）用于联合多个意图检测和填充，其中介绍了一种意图 - 词槽图交互层来模拟词槽和意图之间的强相关性。在三个多意图数据集上的实验结果表明，我们的框架获得了实质性的改进并实现了最新的性能。另外，我们的框架在两个单意图数据集上实现了新的最新性能表现。